Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.     

Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process

An energy-efficient intelligent manufacturing system could significantly save energy compared to traditional intelligent manufacturing systems that do not consider energy issues. Intelligent energy estimation of machining processes is the foundation of the energy-efficient intelligent manufacturing system. This paper proposes a method for machining activity extraction and energy attributes inheritance to support the intelligent energy estimation of machining processes. Fifteen machining activities and their energy attributes are defined according to their operating and energy consumption characteristics. Activities and energy attributes are extracted mainly from NC program supplemented with blank dimensional information. An effective extraction method of activities and energy attributes is the basis for the intelligent energy calculating of machining process. Based on an investigation on the extraction procedure of activities and energy attributes, energy attributes inheritance method is further discussed. Four types of energy attribute inheritance rules are summarized according to the different inheritance characteristics. Based on these four types of inheritance rules, the energy attributes can be transmitted from activity to Therblig as effective inputs of Therblig-based energy model of machining processes. The proposed methodology is finally demonstrated through two machining cases.     

Relative attitude formation control of multi‐agent systems

This paper investigates the relative attitude formation control problem for a group of rigid‐body agents using relative attitude information on SO(3). On the basis of the gradient of a potential function, a family of distributed angular velocity control laws, which differ in the sense of a geodesic distance dependent function, is proposed. With directed and switching interaction topologies, the desired relative attitude formation is showed to be achieved asymptotically provided that the topology is jointly quasi‐strongly connected. Moreover, several sufficient conditions for the desired formation to be achieved exponentially and almost globally are given. Additionally, numerical examples are provided to illustrate the effectiveness of the proposed distributed control laws. Copyright © 2017 John Wiley & Sons, Ltd.     

Image encryption based on random projection partition and chaotic system

Multimedia Tools and Applications - Image encryption is a useful technique of multimedia security and has been widely used in content protection, image authentication, data hiding, and so on. In...     

Reversible data hiding in encrypted images based on multi-level encryption and block histogram modification

Multimedia Tools and Applications - In recent years there has been significant interest in reversible data hiding, and also in particular, reversible data hiding in encrypted images (RDH-EI). This...     

Pricing ad slots with consecutive multi-unit demand

We consider the optimal pricing problem for a model of the rich media advertisement market, that has other related applications. Our model differs from traditional position auctions in that we consider buyers whose demand might be multiple consecutive slots, which is motivated by modeling buyers who may require these to display a large size ad. We study three major pricing mechanisms, the Bayesian pricing model, the maximum revenue market equilibrium model and an envy-free solution model. Under the Bayesian model, we design a polynomial-time computable truthful mechanism that optimizes the revenue. For the market equilibrium paradigm, we find a polynomial-time algorithm to obtain the maximum revenue market equilibrium solution. In the envy-free setting, an optimal solution is presented for the case where the buyers have the same demand for the number of consecutive slots. We present results of a simulation that compares the revenues from the above schemes.     

Twitter summarization with social-temporal context

Twitter is one of the most popular social media platforms for online users to create and share information. Tweets are short, informal, and large-scale, which makes it difficult for online users to find reliable and useful information, arising the problem of Twitter summarization. On the one hand, tweets are short and highly unstructured, which makes traditional document summarization methods difficult to handle Twitter data. On the other hand, Twitter provides rich social-temporal context beyond texts, bringing about new opportunities. In this paper, we investigate how to exploit social-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with social-temporal context for Twitter data. To assess the proposed framework, we manually label a real-world Twitter dataset. Experimental results from the dataset demonstrate the importance of social-temporal context in Twitter summarization.     

Time-variant global reliability sensitivity analysis of structures with both input random variables and stochastic processes

Structural and Multidisciplinary Optimization - The ubiquitous uncertainties presented in the input factors (e.g., material properties and loads) commonly lead to occasional failure of mechanical...     

遗传算法在跨超声速风洞总压控制中的应用

总压作为风洞控制中的重要流场参数,其调节性能是风洞控制系统能否满足试验要求的重要指标,为提高跨超声速风洞的总压控制水平,需对总压控制策略进行设计。针对某跨超声速风洞对总压控制系统提出的快速性和精确性要求,提出串级控制、智能PID控制和总压分段控制等方法,并利用MATLAB系统辨识工具箱对流场调节阶段的总压系统模型进行了辨识。提出将遗传算法应用于风洞流场调节阶段的PID控制器参数整定中,重点对基于遗传算法的PID控制原理和参数整定步骤进行介绍,并针对遗传算法的遗传算子进行了设计。系统仿真和风洞实际运行情况表明：该方法较常规PID参数整定与优化方法,具有更好的控制性能指标,满足总压控制系统精确性、快速性、鲁棒性等要求,为后续风洞建设和设备改造提供了新方法。